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How maps and machine learning are helping to eliminate malaria

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To determine the risk of malaria, DiSARM combines the precise location of the malaria infection,  with satellite data of conditions like rainfall, temperature, vegetation, elevation, which affect mosquito breeding. DiSARM’s mobile app can be used by the malaria programs and field teams to target interventions.

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The DiSARM interface gives malaria programs a near real-time view of malaria and predicts risk at specific locations, such as health facility service areas, villages and schools. Overlaying data allows malaria control programs to identify high-risk areas that have insufficient levels of protection and better distribute their interventions.
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DiSARM’s targeting module uses the risk map to prioritize areas for interventions such as indoor residual spraying (IRS), insecticide treated nets (ITNs) and mass drug administration (MDA).
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